Phase filtering is one of the core signal processing steps in interferometric synthetic aperture radar (InSAR). In recent years, InSAR phase filtering algorithms have evolved from traditional solutions to deep learning (DL) methods, significantly improving the processing efficiency. However, most DL-based phase filtering techniques originate from optical filtering methods, and these methods inevitably entail a tradeoff between noise suppression and detail preservation. To resolve this contradiction and fully take into account the characteristics of InSAR phase, a multiscale phase filtering network (MPFNet) based on multilook information fusion is proposed. First, the network adopts the multiscale structure to balance noise suppression and detail preservation, where the multiscale information is obtained through multilook interferograms of varying numbers of looks. Second, drawing on the mechanism of super-resolution, the network incorporates the residual feature distillation blocks (RFDBs) to restore the scale of interferograms. Finally, in response to the demand for complex phase filtering, a loss function based on cosine similarity is constructed, which avoids the discontinuity at$\pm \pi $affecting the filtering results. Computer simulation and experiments based on real InSAR data verified the effectiveness of the proposed method.

Gesture recognition of millimeter-wave radar plays an important role in human-computer interaction within the current Internet of Everything context. Based on the signal pre-processing algorithm of MUSIC, this paper proposes a gesture recognition algorithm using error-correction code(ECC) module. Firstly, the gesture signals collected by millimeter-wave radar are processed into three sets of images of azimuth, elevation and depth by using signal processing algorithms such as MUSIC; then they are sent to the feature extraction network and output the discriminant code; finally, the discriminant codes are sent to ECC module to output the final verdict result. The addition of error correction code makes the whole decision mechanism more logical and robust, and it may also significantly enhance the real-time performance of gesture detection since it avoids the complicated network.

A ground-based radar is a potential technique for lunar surface imaging. However, due to the Earth’s rotation, the maximum azimuth resolution is limited for a single observation. To solve this problem, this letter analyzes the feasibility and performance of obtaining multiangle data from different observations and forms large virtual apertures through aperture synthesis. To ensure the quality of synthesized images, an observation baseline selection method is proposed based on the principle of spectrum continuity, specifying that for two noncontinuous observations, there should be a point on each of them whose target-to-radar vectors share the same direction. Besides, an aperture synthesis algorithm based on spectrum compression is proposed to eliminate spectrum aliasing. The effectiveness of the algorithm is verified via computer simulations and real data experiments. By forming a well-focused 500-m resolution image from two noncontinuous observations, the validation of the proposed algorithm has been proved.

Multistatic unmanned aerial vehicle synthetic aperture radar (MUAV-SAR) three-dimensional (3-D) imaging system suffers from the time and phase synchronization errors among multiple stations. The classical two-way direct wave pulse exchange synchronization method introduces the π-ambiguity phase error and causes limited time-phase synchronization accuracy with multiple system nodes, leading to 3-D images defocusing. An MUAV-SAR joint synchronization method based on multiple direct wave pulses exchange is proposed to solve the π-ambiguity problem robustly and improve the synchronization accuracy significantly. Firstly, the π-ambiguity phase error is estimated through the comparative calculation of delay-phase information extracted from direct wave pulses and the high estimated success probability (99.73%) of the π-ambiguity can be achieved through the noise smoothing. Secondly, the synchronization accuracy is improved, that is, the time and phase error are reduced to about √2/Nof the existing method by utilizing N stations information fusion to jointly process redundant information of direct wave pulses from multiple synchronization links. Finally, a four-station UAV SAR real data experiment verifies the effectiveness of the proposed approach.

Due to the limited accuracy of navigation systems, airborne InSAR processing is affected by residual motion error (RME). The phase-based methods such as multi-squint are usually based on the assumption that the RME is quite small. Different from dual-antenna InSAR systems, larger RME occurs in repeat-pass InSAR experiments since the RMEs are independent between two images, which causes decorrelation and reduces compensation accuracy in the phase-based method. With the improvement of SAR image resolution in recent years, the accuracy of co-registration is also improved. In this paper, a method for estimating the residual rotation angle of radar trajectory based on co-registration offset is proposed to compensate the RME when facing decorrelation. After that, the entire process flow of compensating RME is given. The computer simulation results have been conducted to verify the effectiveness of the proposed approach.

Multichannel parameter estimation is a key technology for azimuth multichannel terrain observation by progressive scans synthetic aperture radar (MC TOPS SAR) systems. Normally, for the traditional multichannel parameter estimation algorithms, the mismatch parameters are concluded as phase error, baseline error, and Doppler centroid. The beam rotation speed error is ignored. However, for MC TOPS SAR, beam rotation speed error will degrade the reconstruction performance of the Doppler spectrum. Moreover, only part of the mismatch parameters can be estimated for the traditional algorithms. To address those problems, a joint parameters estimation algorithm for TOPS SAR based on spatial time cross correlation coefficient (STCCC) is proposed. Combined with phase wrapping and the least-squares method (LSM), the algorithm is capable of realizing a joint parameters estimation of channel consistency error, azimuth baseline error, Doppler centroid frequency, and beam rotation speed estimation without iteration. The real data processing result shows that the method can effectively realize MC TOPS SAR data error estimation.

Spaceborne multi-channel synthetic aperture radar (SAR) is an effective means to realize high-resolution and wide-swath imaging. However, for spaceborne multi-channel SAR imaging of maritime moving targets, the target motion will cause undesired channel imbalance, i.e., phase error, and further introduce the spurious targets in the image. To solve this problem, this paper proposes a novel spaceborne multi-channel SAR imaging algorithm for maritime moving targets, which consists of sequential coarse imaging and accurate imaging. The key strategies are to separate different moving targets by coarse imaging and to estimate the phase error based on the relationship between phase errors and amplitudes of spurious targets. First, the quantitative relationship between phase errors and amplitudes of spurious targets is established. Second, based on coarse imaging results, different maritime targets are effectively separated. Then, based on the measured amplitudes of spurious targets, a cost function, which represents the difference between the real target velocity and estimated target velocity, is constructed and minimized to separately estimate the velocities and phase errors of targets. Moreover, to further improve the accuracy of estimation and to suppress the undesired effects caused by target defocusing, clutter, and noise, an iterative strategy is adopted. Last, by auto-focusing, a well-focused SAR image is obtained. The GF-3 dual-channel real data experiment is conducted. The results indicate that the spurious targets are well suppressed, which validates the effectiveness of the proposed algorithm.

Synthetic aperture radar (SAR) sometimes needs to be imaged with small time bandwidth product (TBP) for fast observation. As a real-time image algorithm, back-projection algorithm (BPA) is efficient for fast observation. However, compared with the frequency-domain algorithm, the influence of small TBP (STBP) on BPA has not been fully discussed. Thus, the performance of the original BPA on STBP SAR imaging is analyzed and an improved BPA for STBP imaging is proposed in this letter. Theoretical analysis indicates that STBP will cause the point spread function (PSF) distortion and azimuth spectrum ambiguity to the BPA imaging result. To deal with those problems, two parameters of the original BPA are modified in the improved BPA. The accumulating azimuth angle (AAA) is optimized first to calibrate the PSF distortion. Then, based on the azimuth-ambiguity-to-signal ratio (AASR), a BPA meshing criterion related to TBP is proposed to diminish azimuth spectrum ambiguity. The simulation and real data results demonstrate that the improved BPA is effective on undistorted PSF recovering and unambiguity spectrum maintaining, even when the TBP is down to 4.

Distributed multichannel synthetic aperture radar (MC-SAR) is a system in which transmitting or receiving arrays are distributed on multiple platforms or at different locations on one platform. The along-track component of the baseline makes distributed SAR promising in high-resolution wide-swath (HRWS) imaging such as azimuth MC-SAR. However, the additional channel mismatch introduced by the cross-track baseline (CTB) is considered for the distributed SAR. When the azimuth beam is wide, the azimuth-variant channel mismatch caused by the CTB must be compensated before SAR imaging. First, an improved azimuth signal reconstruction algorithm for distributed wide-beam SAR is proposed in this paper. The azimuth variance of the channel mismatch is considered in a reconstruction filter to further suppress the ambiguity, and the computational consumption is decreased by approximately decomposing the mismatch matrix. Second, the ambiguity suppression performance of the proposed method is analyzed quantitatively. Finally, a simulation and real data processing are provided to demonstrate the effectiveness of the proposed method.

High-squint imaging is an effective means to enhance the flexibility and coverage ability of spaceborne synthetic aperture radar (SAR). Although existing imaging algorithms based on linear range cell migration correction (LRCMC) and nonlinear chirp scaling (NCS) can reduce the range-azimuth coupling of the spectrum and the spatial-variant of the Doppler parameter to some extent, they become invalid as the resolution increases. On one hand, the beam rotation of sliding spotlight SAR results in nonlinear azimuth-variant of the Doppler centre, and the traditional deramping operation, which removes the linear variation, will cause spectrum aliasing. On the other hand, these algorithms assume the azimuth-variants of range cell migration (RCM) are consistent in the total swath. However, the azimuth-variants of RCM are different in different range cells, which cannot be neglected in high-resolution imaging. To solve these problems, a novel imaging algorithm based on two-dimensional spatial-variant range cell migration correction is proposed in this paper. First, LRCMC is utilized, and the nonlinear azimuth deramping operation is conducted to obtain aliasing-free spectrum. Then, the azimuth-variant of RCM is corrected by azimuth interpolation and polynomial compensation. Noting that the interpolation coefficient varies linearly with slant range, this can weaken the azimuth-variant differences of RCM in different range cells. Meanwhile, azimuth polynomial compensation can correct the consistent azimuth-variant of RCM, and hence the azimuth-variant of RCM can be totally corrected. Finally, the compression is performed via the range chirp scaling and azimuth NCS. The effectiveness of the proposed algorithm is verified by computer simulations.

Combined with terrain observation by progressive scans (TOPS) synthetic aperture radar (SAR), near-nadir SAR (N-SAR) system has great potential for the surface water and ocean topography (SWOT) observation. However, one practical problem of the airborne N-SAR is the non-ideal attitude angle caused by the environment. Normally, the influence of attitude angle error can be considered as a constant squint angle of the system. However, in fact, for the near-nadir SAR system with TOPS SAR mode, the attitude angle error will cause a non-ideal 2-dimensional space-variant Doppler centroid for the receiving data and cause the traditional imaging algorithm failure. To deal with such problems, an imaging algorithm with attitude angle estimation for the near-nadir TOPS SAR system is proposed in this paper. Firstly, the influence of attitude angle error and the signal properties are analyzed. Secondly, based on the analysis, an attitude angle estimate algorithm (AAE), utilizing the nonlinear least square method (NLSM) and minimum entropy criterion, is proposed. Then, based on the estimated altitude angle, without data blocking, a full data imaging algorithm (FDA) is proposed. Its main idea is to obtain an un-ambiguous azimuth spectrum and design an appropriate range-variant azimuth filter through frequency chirp scaling (FCS). The numerical simulations and real data processing demonstrate that the proposed algorithm has good performance on attitude angle error estimation and well-focused image obtaining.

Combined with Terrain Observation by Progressive Scans (TOPS) Synthetic Aperture Radar (SAR), Near-nadir Interferometry SAR (NInSAR) has great potential on the Surface Water and Ocean Topography (SWOT) observation. One practical problem is that the Doppler center of airborne NInSAR varies along range and its bandwidth is greater than pulse repeat frequency (PRF) due to the affection of yaw angle error. Traditional imaging algorithms divide the data into several range blocks so that the range variation of Doppler center is ignorable. However, the drawback is that the size and the overlap of the blocks are hard to design. In this paper, an improved image algorithm without range block is proposed. The main procedure is to obtain unambiguous azimuth spectrum and design appropriate range varied azimuth filter through frequency chirp scaling algorithm. The good performance of the proposed algorithm is demonstrated through point target simulation and real data.

Vegetation canopy height inversion is a significant research content in Polarimetric Interferometry Synthetic Aperture Radar (PolInSAR) surface parameter inversion. One practical challenge is that the traditional three-stage inversion method will lead to the inaccurate inversion of vegetation canopy height in X-band because of the too concentrated distribution of polarization interference coherence coefficients (PolInCc). In this paper, an X-band PolInSAR vegetation canopy height inversion strategy is proposed based on Frequency Segmentation (FS). The FS divides the original PolInSAR images into multiple PolInSAR images called sub-images, and the sub-images are used to estimate the more near-linear PolInCc distribution of the sub-images by interferometry. Then the precise terrain phase and effective volume coherence coefficient can be obtained to invert the vegetation canopy height accurately. The validity of the proposed method is verified by N-SAR X-band real data.